The AI journal selection platform is under preparation for launch, focusing on journal issue matching.

Which journal is suitable for submitting the paper and whether the publication schedule can meet one's own time arrangement are two issues that authors need to consider simultaneously when selecting journals. Centered on this demand, the AI journal selection platform plans to incorporate research directions and expected publication time into journal matching to help authors narrow down the scope of submission options.

According to the team, the project was launched in 2026, the platform construction has been completed at present, and the preparations for launch and seed user testing are underway. The project is initiated by Zhang Kai, who has more than ten years of experience in academic publishing. At this stage, the project mainly relies on the team's self-raised funds and is looking for investors to meet the capital demand for subsequent marketing promotion. The specific financing round, target amount and transaction progress have not yet been clarified.

Extend from research direction matching to publication time demand

In Zhang Kai's industry observations, the difficulty in journal selection for some authors comes from scattered information: after understanding the research field of a journal, it is necessary to further confirm the submission requirements, review cycle and publication arrangement. For authors with a clear deadline for achievement submission, a research direction that meets the requirements does not mean that the publication schedule can also meet their needs.

The AI journal selection platform takes such time constraints as the entry point of the product. According to the team, the platform will recommend candidate journals by combining the author's research direction and specified publication time, aiming to reduce the costs for authors of querying one by one, communicating repeatedly, and resubmitting due to inappropriate journal selection.

The value of this service must be built on the basis of accurate and timely updated journal information. In particular, the "publication time" needs to distinguish different nodes such as acceptance, online first publication and formal printed publication. Journal matching can provide a reference for submission decisions, but it cannot be inferred as a promise of guaranteed acceptance or publication on schedule.

At present, the team has not provided the specific technical scheme of AI matching, the source of publication schedule data and update mechanism, nor disclosed the quantitative test results of recommendation accuracy and time prediction effect. These pieces of information will affect the public's judgment on the product's capabilities.

Verify differentiated value for authors with clear submission needs

The platform plans to serve graduate students, scientific researchers, and professional and technical personnel who have the need to publish papers under the requirements of specific professional title evaluation. Among them, authors with clear submission plans and time requirements are the users that the project hopes to reach first.

The 2024 National Statistical Communiqué on the Development of Education issued by the Ministry of Education shows that 1.0836 million graduate students graduated in that year. This data reflects the scale of the relevant group, but cannot be directly used as the number of users or market size of journal selection services; the actual demand still depends on the characteristics of disciplines, assessment requirements, submission frequency and willingness to pay.

There are already relevant product layouts for intelligent journal selection. For example, Wanfang Zhiyan has publicly introduced the function of recommending submission journals based on titles and keywords. Therefore, what the AI journal selection platform needs to further prove is its coverage of publication schedule information, update efficiency, and the help of matching results for actual submission decisions.

According to the team, the platform has invited some seed users to participate in the test and received positive feedback on the matching function. However, the existing materials have not provided the number of test participants, covered disciplines, recommendation adoption rate or subsequent submission results, and the product effect still needs to be verified by more actual usage data.

Team is advancing journal connection and business model remains to be clarified

At present, the team is led by the founder to promote the project, the technical team is responsible for platform development and maintenance, and the business team is responsible for connecting with journals and promoting cooperation. Zhang Kai's experience in academic publishing is the basis for the project to identify user needs and carry out industry communication; the specific team size, members' resumes and implemented cooperation situations have not been further disclosed.

In terms of commercialization, the platform plans to connect authors' journal selection needs with journal information, but the existing materials have not specified the payer, paid items and pricing methods, nor provided revenue and profit data. It still needs to be clarified by the team whether journals are charged for entry, whether authors pay for recommendation services, and how commercial cooperation affects the recommendation ranking.

After the platform is launched, the team plans to reach target users through academic conferences and short video channels, and continue to promote journal cooperation. For this project that is preparing for launch, the next key is to answer three questions with actual data: whether the publication schedule information can be continuously and accurately updated, whether the recommendation results can help authors reduce ineffective journal selection, and whether users are willing to pay for this service.